{"id":"https://openalex.org/W3043331788","doi":"https://doi.org/10.1109/ssp49050.2021.9513823","title":"Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis","display_name":"Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3043331788","doi":"https://doi.org/10.1109/ssp49050.2021.9513823","mag":"3043331788"},"language":"en","primary_location":{"id":"doi:10.1109/ssp49050.2021.9513823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp49050.2021.9513823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Statistical Signal Processing Workshop (SSP)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2007.06862","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073843891","display_name":"Khuram Naveed","orcid":"https://orcid.org/0000-0001-8286-6139"},"institutions":[{"id":"https://openalex.org/I16076960","display_name":"COMSATS University Islamabad","ror":"https://ror.org/00nqqvk19","country_code":"PK","type":"education","lineage":["https://openalex.org/I16076960"]}],"countries":["PK"],"is_corresponding":true,"raw_author_name":"Khuram Naveed","raw_affiliation_strings":["COMSATS University Islamabad (CUI), Islamabad, Pakistan","COMSATS institute of information technology"],"affiliations":[{"raw_affiliation_string":"COMSATS University Islamabad (CUI), Islamabad, Pakistan","institution_ids":["https://openalex.org/I16076960"]},{"raw_affiliation_string":"COMSATS institute of information technology","institution_ids":["https://openalex.org/I16076960"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049620222","display_name":"Sidra Mukhtar","orcid":null},"institutions":[{"id":"https://openalex.org/I16076960","display_name":"COMSATS University Islamabad","ror":"https://ror.org/00nqqvk19","country_code":"PK","type":"education","lineage":["https://openalex.org/I16076960"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Sidra Mukhtar","raw_affiliation_strings":["COMSATS University Islamabad (CUI), Islamabad, Pakistan","COMSATS University Islamabad (CUI),Department of Electrical and Computer Engineering,Islamabad,Pakistan"],"affiliations":[{"raw_affiliation_string":"COMSATS University Islamabad (CUI), Islamabad, Pakistan","institution_ids":["https://openalex.org/I16076960"]},{"raw_affiliation_string":"COMSATS University Islamabad (CUI),Department of Electrical and Computer Engineering,Islamabad,Pakistan","institution_ids":["https://openalex.org/I16076960"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054557758","display_name":"Naveed ur Rehman","orcid":"https://orcid.org/0000-0002-3700-5839"},"institutions":[{"id":"https://openalex.org/I16076960","display_name":"COMSATS University Islamabad","ror":"https://ror.org/00nqqvk19","country_code":"PK","type":"education","lineage":["https://openalex.org/I16076960"]},{"id":"https://openalex.org/I204337017","display_name":"Aarhus University","ror":"https://ror.org/01aj84f44","country_code":"DK","type":"education","lineage":["https://openalex.org/I204337017"]}],"countries":["DK","PK"],"is_corresponding":false,"raw_author_name":"Naveed Ur Rehman","raw_affiliation_strings":["Aarhus University, Aarhus, Denmark","COMSATS institute of information technology"],"affiliations":[{"raw_affiliation_string":"Aarhus University, Aarhus, Denmark","institution_ids":["https://openalex.org/I204337017"]},{"raw_affiliation_string":"COMSATS institute of information technology","institution_ids":["https://openalex.org/I16076960"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5073843891"],"corresponding_institution_ids":["https://openalex.org/I16076960"],"apc_list":null,"apc_paid":null,"fwci":0.591,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.79874619,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"441","last_page":"445"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10244","display_name":"Chaos control and synchronization","score":0.989799976348877,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12946","display_name":"Fractal and DNA sequence analysis","score":0.9872999787330627,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.7879235744476318},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6678093671798706},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5811939835548401},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5806511640548706},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5779690146446228},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5476455092430115},{"id":"https://openalex.org/keywords/detrended-fluctuation-analysis","display_name":"Detrended fluctuation analysis","score":0.5394047498703003},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.5341042280197144},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5310540199279785},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48748302459716797},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.46993786096572876},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4250532388687134},{"id":"https://openalex.org/keywords/multivariate-analysis","display_name":"Multivariate analysis","score":0.41390347480773926},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.344096839427948},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.32181185483932495},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07689312100410461},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.06630682945251465}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7879235744476318},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6678093671798706},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5811939835548401},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5806511640548706},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5779690146446228},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5476455092430115},{"id":"https://openalex.org/C21689155","wikidata":"https://www.wikidata.org/wiki/Q2451452","display_name":"Detrended fluctuation analysis","level":3,"score":0.5394047498703003},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.5341042280197144},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5310540199279785},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48748302459716797},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.46993786096572876},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4250532388687134},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.41390347480773926},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.344096839427948},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.32181185483932495},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07689312100410461},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.06630682945251465},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/ssp49050.2021.9513823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp49050.2021.9513823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Statistical Signal Processing Workshop (SSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2007.06862","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.06862","pdf_url":"https://arxiv.org/pdf/2007.06862","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3043331788","is_oa":true,"landing_page_url":"https://aps.arxiv.org/pdf/2007.06862","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:pure.atira.dk:publications/2f47118f-741a-4a2d-bfd2-473e44e07c7b","is_oa":false,"landing_page_url":"http://www.scopus.com/inward/record.url?scp=85113522919&partnerID=8YFLogxK","pdf_url":null,"source":{"id":"https://openalex.org/S4306400063","display_name":"Scopus (Elsevier)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Naveed, K, Mukhtar, S & Ur Rehman, N 2021, Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis. in 2021 IEEE Statistical Signal Processing Workshop, SSP 2021. IEEE, pp. 441-445, 21st IEEE Statistical Signal Processing Workshop, SSP 2021, Virtual, Rio de Janeiro, Brazil, 11/07/2021. https://doi.org/10.1109/SSP49050.2021.9513823","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"doi:10.48550/arxiv.2007.06862","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2007.06862","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2007.06862","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.06862","pdf_url":"https://arxiv.org/pdf/2007.06862","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/12","display_name":"Responsible consumption and production","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W191129667","https://openalex.org/W1999377952","https://openalex.org/W2000607090","https://openalex.org/W2000982976","https://openalex.org/W2008278511","https://openalex.org/W2013046778","https://openalex.org/W2017821362","https://openalex.org/W2026973890","https://openalex.org/W2052172657","https://openalex.org/W2103484453","https://openalex.org/W2106782121","https://openalex.org/W2109896502","https://openalex.org/W2123563890","https://openalex.org/W2230410254","https://openalex.org/W2283544056","https://openalex.org/W2345466586","https://openalex.org/W2518159573","https://openalex.org/W2563757121","https://openalex.org/W2582131869","https://openalex.org/W2958872067","https://openalex.org/W2966342215","https://openalex.org/W3016420584","https://openalex.org/W3026372945","https://openalex.org/W3094291701"],"related_works":["https://openalex.org/W3194946604","https://openalex.org/W2582131869","https://openalex.org/W2283544056","https://openalex.org/W2744000381","https://openalex.org/W2048032753","https://openalex.org/W2535304488","https://openalex.org/W2841644753","https://openalex.org/W2119522267","https://openalex.org/W2164467956","https://openalex.org/W2078258947","https://openalex.org/W2884631034","https://openalex.org/W3112007101","https://openalex.org/W2377427772","https://openalex.org/W1587616920","https://openalex.org/W2969214403","https://openalex.org/W2353023204","https://openalex.org/W3196786865","https://openalex.org/W2370332490","https://openalex.org/W3110399007","https://openalex.org/W2372954482"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,31],"novel":[3,32],"multivariate":[4,35,62,72],"signal":[5,59,90],"denoising":[6,52],"method":[7,42,53],"that":[8],"performs":[9],"long-range":[10],"correlation":[11],"analysis":[12,40,110],"of":[13,24,37,46,102],"multiple":[14],"modes":[15,81,97],"in":[16],"input":[17],"data":[18,56],"by":[19,93],"considering":[20],"inherent":[21],"inter-channel":[22],"dependencies":[23],"the":[25,69,78,88,95,100,103,107],"data.":[26],"That":[27],"is":[28,74,91],"achieved":[29],"through":[30],"and":[33],"generic":[34,71],"extension":[36],"detrended":[38],"fluctuation":[39],"(DFA)":[41],"-":[43],"another":[44],"contribution":[45],"this":[47],"paper.":[48],"Specifically,":[49],"our":[50],"proposed":[51,70],"first":[54],"obtains":[55],"driven":[57],"multiscale":[58],"representation":[60],"using":[61,106],"variational":[63],"mode":[64],"decomposition":[65],"(MVMD)":[66],"method.":[67],"Then,":[68],"DFA":[73],"used":[75],"to":[76],"reject":[77],"predominantly":[79],"noisy":[80],"based":[82],"on":[83],"their":[84],"randomness":[85],"scores.":[86],"Finally,":[87],"denoised":[89],"reconstructed":[92],"summing":[94],"remaining":[96],"albeit":[98],"after":[99],"removal":[101],"noise":[104],"traces":[105],"principal":[108],"component":[109],"(PCA).":[111]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
